2019
DOI: 10.3389/fpls.2019.00924
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Identifying Verticillium dahliae Resistance in Strawberry Through Disease Screening of Multiple Populations and Image Based Phenotyping

Abstract: Cockerton et al. Verticillium Wilt Resistance in Strawberry germplasm. Furthermore, comparison of imaging versus manual phenotyping revealed the automated platform could identify three out of four disease resistance markers. As such, this automated wilt disease phenotyping platform is considered to be a good, time saving, substitute for manual assessment.

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Cited by 26 publications
(19 citation statements)
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“…The simple (gene‐for‐gene) solution to the problem of developing resistant cultivars has not been feasible in strawberry because R ‐genes conferring resistance to Verticillium wilt have not been identified (Maas, Galletta, & Draper, 1989; Vining, Davis, Jamieson, & Mahoney, 2015). Large‐effect quantitative trait loci (QTL) have not been identified either (Antanaviciute et al., 2015; Cockerton et al., 2019). Consequently, phenotypic selection for quantitative resistance to this pathogen has previously been the only solution (Bringhurst, Hansche, & Voth, 1968; Darrow, 1966; Maas et al., 1989; Shaw, Gubler, Larson, & Hansen, 1996, 2010).…”
Section: Introductionmentioning
confidence: 99%
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“…The simple (gene‐for‐gene) solution to the problem of developing resistant cultivars has not been feasible in strawberry because R ‐genes conferring resistance to Verticillium wilt have not been identified (Maas, Galletta, & Draper, 1989; Vining, Davis, Jamieson, & Mahoney, 2015). Large‐effect quantitative trait loci (QTL) have not been identified either (Antanaviciute et al., 2015; Cockerton et al., 2019). Consequently, phenotypic selection for quantitative resistance to this pathogen has previously been the only solution (Bringhurst, Hansche, & Voth, 1968; Darrow, 1966; Maas et al., 1989; Shaw, Gubler, Larson, & Hansen, 1996, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…With recent advances in the development of genomic resources and supporting information (Edger et al., 2019; Hardigan et al., 2020; Whitaker et al., 2020), strawberry is well positioned for the application of genome‐informed breeding approaches (Crossa et al., 2017; Poland & Rutkoski, 2016). Here, we explore the prospects for increasing genetic gains and accelerating the development of Verticillium wilt resistant cultivars through the application of genomic selection, under the assumption that resistance to this pathogen is genetically complex in cultivated strawberry (Antanaviciute et al., 2015; Cockerton et al., 2019; Shaw et al., 1996; Vining et al., 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Similar resistance responses have been observed within and outside the Solanaceae family for various Ve1 homologs [ 64 ]. However, several studies have reported that VW resistance is controlled by additive quantitative genetic components located in multiple small effect genomic loci (quantitative trait locus, QTLs) [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ]. Among these, the QTL for V. longisporum resistance in oilseed rape co-localized with QTLs for a number of phenylpropanoids [ 72 ].…”
Section: Discussionmentioning
confidence: 99%
“…The strawberry (Fragaria x ananassa) 'Redgauntlet' x 'Hapil' mapping population was used to study root architecture, AMF association and low phosphate tolerance. Plant material was generated at NIAB EMR, the F1 cross was initially made to study Verticillium dahliae resistance [48]. For the initial assessment of parental root architecture, 10 runner plants of 'Redgauntlet' and 'Hapil' were pinned down into rhizotrons.…”
Section: Root Architecture Quantification Plant Materialsmentioning
confidence: 99%
“…QTL identified in both analysis are considered to be robust and thus reported here. Heritability and proportional reduction of error was calculated as specified in Cockerton et al, 2019 [48]. Principal component analysis was used to determine the components accounting for the largest proportion of variation in genotypes.…”
Section: Quantitative Trait Loci (Qtl) Analysismentioning
confidence: 99%